In the previous chapter, we implemented an intelligent agent that used Q-learning to solve the Mountain Car problem from scratch in about seven minutes on a dual-core laptop CPU. In this chapter, we will implement an advanced version of Q-learning called deep Q-learning, which can be used to solve several discrete control problems that are much more complex than the Mountain Car problem. Discrete control problems are (sequential) decision-making problems in which the action space is discretized into a finite number of values. In the previous chapter, the learning agent used a 2-dimensional state-space vector as the input, which contained the information about the ...
Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
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